Social Control and Interactivity in Anonymous Public Events
Bibliographic record
Abstract
Online event hosting platforms, such as Zoom Meetings and Twitch Streams, have revolutionized the way we socialize with one another. These platforms offer a rich set of interactive features such as live chat and gestures, enabling dynamic and engaging social events. In public events, however, participants are not well-known entities originating from the same institution, and thus traditional access control fails to provide means for maintaining order without disrupting interactivity. Zoombombing and cyberbullying in Twitch Streams are symptoms of this dilemma. The design of the aforementioned event hosting systems thus resort to social control mechanisms that allow moderators to monitor the social interactions of the participants and respond to disorderly behavior in real time. The designer of an event hosting system needs to make sure that social control mechanisms preserve interactivity expectations. In this paper, we introduce HIPE (Highly Interactive Public Event), a framework for modelling social control mechanisms, articulating interactivity expectations, as well as verifying if social control interferes with interactivity. We catalogued 4 classes of social control mechanisms that can be reused in the design of event hosting systems, including sanction, remedy, containment, and retaliation. Additionally, we formulated a 2-safety hyperproperty known as ( a , p )-interactivity, for expressing the degree of interactivity expected of an event hosting system. Furthermore, we designed a model checking algorithm for verifying ( a , p )-interactivity. An empirical case study has been conducted to illustrate the interplay between social control and interactivity, as well as to evaluate the performance of our model checking algorithm. To the best of our knowledge this is the first work to formally study the balancing of social control and interactivity in public events.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".